The lack of new drugs for Gram-negative pathogens is a global threat to modern medicine. The complexity of their cell envelope, with an additional outer membrane, hinders internal accumulation and thus, the access of molecules to their targets. Our limited understanding of the molecular basis for compound influx and efflux from these pathogens is a major bottleneck for the discovery of effective antibacterial compounds. Here we analyse the correlation between the whole-cell compound accumulation of ~200 molecules and their predicted porin permeability coefficient (influx), using a recently developed scoring function. We found a strong linear relationship (74%) between the two, confirming porins key in compound uptake in Gram-negative bacteria. The analysis of this unique dataset aids to better understand the molecular descriptors behind whole-cell accumulation and molecular uptake in Gram-negative bacteria.Wildfires are becoming more frequent in different parts of the globe, and the ability to predict when and where they will occur is a complex process. Identifying wildfire events with high probability of becoming a large wildfire is an important task for supporting initial attack planning. Different methods, including those that are physics-based, statistical, and based on machine learning (ML) are used in wildfire analysis. Among the whole, those based on machine learning are relatively novel. In addition, because the number of wildfires is much greater than the number of large wildfires, the dataset to be used in a ML model is imbalanced, resulting in overfitting or underfitting the results. In this manuscript, we propose to generate synthetic data from variables of interest together with ML models for the prediction of large wildfires. Specifically, five synthetic data generation methods have been evaluated, and their results are analyzed with four ML methods. The results yield an improvement in the prediction power when synthetic data are used, offering a new method to be taken into account in Decision Support Systems (DSS) when managing wildfires.The increasing demand for organ replacements in a growing world with an aging population as well as the loss of tissues and organs due to congenital defects, trauma and diseases has resulted in rapidly evolving new approaches for tissue engineering and regenerative medicine (TERM). The extracellular matrix (ECM) is a crucial component in tissues and organs that surrounds and acts as a physical environment for cells. Thus, ECM has become a model guide for the design and fabrication of scaffolds and biomaterials in TERM. However, the fabrication of a tissue/organ replacement or its regeneration is a very complex process and often requires the combination of several strategies such as the development of scaffolds with multiple functionalities and the simultaneous delivery of growth factors, biochemical signals, cells, genes, immunomodulatory agents, and external stimuli. Although the development of multifunctional scaffolds and biomaterials is one of the most studied approaches for TERM, all these strategies can be combined among them to develop novel synergistic approaches for tissue regeneration. In this review we discuss recent advances in which multifunctional scaffolds alone or combined with other strategies have been employed for TERM purposes.Retinitis pigmentosa (RP) is an inherited retinopathy. Nevertheless, non-genetic biological factors play a central role in its pathogenesis and progression, including inflammation, autophagy and oxidative stress. The retina is particularly affected by oxidative stress due to its high metabolic rate and oxygen consumption as well as photosensitizer molecules inside the photoreceptors being constantly subjected to light/oxidative stress, which induces accumulation of ROS in RPE, caused by damaged photoreceptor's daily recycling. Oxidative DNA damage is a key regulator of microglial activation and photoreceptor degeneration in RP, as well as mutations in endogenous antioxidant pathways involved in DNA repair, oxidative stress protection and activation of antioxidant enzymes (MUTYH, CERKL and GLO1 genes, respectively). Moreover, exposure to oxidative stress alters the expression of micro-RNA (miRNAs) and of long non-codingRNA (lncRNAs), which might be implicated in RP etiopathogenesis and progression, modifying gene expression and cellular response to oxidative stress. The upregulation of the P2X7 receptor (P2X7R) also seems to be involved, causing pro-inflammatory cytokines and ROS release by macrophages and microglia, contributing to neuroinflammatory and neurodegenerative progression in RP. The multiple pathways analysed demonstrate that oxidative microglial activation may trigger the vicious cycle of non-resolved neuroinflammation and degeneration, suggesting that microglia may be a key therapy target of oxidative stress in RP.Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC 0.748, p = 0.132). https://www.selleckchem.com/products/en450.html There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future.